Many elements within standard cells, memory, and I/O follow similar trends under differing conditions, for example: cell families have similar topology but are just sized differently, differing arcs often show similar behaviors, and PVT (process, voltage, temperature) corners show similar trends but are shifted, scaled or skewed.
Because of these similar trends, library characterization can be accelerated with machine learning technologies. Existing data can be mined for trends, and new library models can be built from previously characterized libraries, but as these library models are built, there is also a need to ensure Monte Carlo accurate LVF/AOCV/POCV library models.
Solido, an EDA industry leader in machine learning technologies, has newly launched ML Characterization Suite--an exciting new tool able to meet these increasing characterization demands. Their continuing success with Variation Designer has placed them in a unique position to develop tools to leverage large scale trends in characterization and mine existing data for trends, all while keeping accuracy paramount.
Within Solido ML Characterization Suite are Predictor and Statistical Characterizer products. Predictor works by:
- Reading in existing characterized libraries,
- Determining the PVT corner conditions and turning them into variables and values,
- Building regression models to predict Liberty values at other PVT conditions,
- Writing out new Liberty files at new PVT corners.
Solido ML Characterization Suite Predictor has been shown by customers in production to reduce library characterization runtimes by 30% to 70% without compromising accuracy. It’s now instantly possible to produce more libraries for additional PVT corners, saving days to weeks in characterization time and saving on SPICE simulator and characterization resources. Predictor is easy to add into any characterization flow, and works with all Liberty data - NLDM, CCS, CCSN, AOCV, LVF, ECSM, etc.
The second component in ML Characterization Suite is Statistical Characterizer. Statistical Characterizer works by:
- Reading existing libraries without LVF/AOCV/POCV statistical data,
- Selecting simulations to produce accurate LVF/AOCV/POCV data for all corners and to parallelize simulations efficiently,
- Adaptively selecting additional simulations where more accuracy is needed,
- Writing existing libraries with LVF/AOCV/POCV added.
This technique generates statistical library models with Monte Carlo and SPICE accuracy over 1000 times faster than brute force Monte Carlo. Statistical Characterizer also precisely handles non-Gaussian distributions, all while delivering true 3-sigma LVF/AOCV/POCV.
For even greater statistical characterization savings, Statistical Characterizer and Predictor can be used in tandem for fast, accurate statistical characterization. By using Statistical Characterizer only on anchor corners to quickly add Monte Carlo accurate LVF/AOCV/POCV, then running Predictor to create the remaining corners, Statistical Characterizer only needs to be run on half of the PVT conditions.
ML Characterization Suite Predictor and Statistical Characterizer are available immediately. Sign up here for a 15min demo.
About Solido Design Automation
Solido Design Automation Inc. is a leading provider of variation-aware design software for high yield and performance IP and systems-on-a-chip (SOCs). Solido plays an essential role in de-risking the variation impacts associated with the move to advanced and low-power processes, providing design teams improved power, performance, area and yield for memory, standard cell, analog/RF, and custom digital design. Solido’s efficient software solutions address the exponentially increasing analysis required without compromising time-to-market. The privately held company is venture capital funded and has offices in the USA, Canada, Asia and Europe. For further information, visit www.solidodesign.com or call 306-382-4100.